The architecture of our network was presented in a method for medical image registration [1]}, and consists in an encoder-decoder CNN with skip connections. The encoder part is composed of four down-sampling blocks, which capture the hierarchical features necessary to establish correspondence between the image pair. Each down-sampling block consists of two convolutional layers with a residual network shortcut, batch normalization, and leaky rectified linear unit (Leaky ReLU). Symmetrically, the decoder part is composed of four up-sampling blocks that consists of an additive up-sampling layer summed over a transpose convolutional layer. Finally, each up-sampling block outputs a displacement field that is convoluted and resized to the input size, then summed to output the predicted displacement field. The network was implemented in TensorFlow using NiftyNet [2]}. It was trained by using the Adam optimiser, starting at a learning rate of 10-3, with a minibatch of 4. The regularisation weight was set to \(\alpha =2\)  to ensure displacement continuity.
